Overview

Dataset statistics

Number of variables9
Number of observations12120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory852.3 KiB
Average record size in memory72.0 B

Variable types

TimeSeries5
Numeric4

Alerts

tmin is highly overall correlated with tmax and 2 other fieldsHigh correlation
tmax is highly overall correlated with tmin and 2 other fieldsHigh correlation
tmed is highly overall correlated with tmin and 2 other fieldsHigh correlation
presMin is highly overall correlated with presMaxHigh correlation
presMax is highly overall correlated with tmin and 1 other fieldsHigh correlation
velmedia is highly overall correlated with rachaHigh correlation
racha is highly overall correlated with velmediaHigh correlation
sol is highly overall correlated with tmax and 1 other fieldsHigh correlation
tmin is non stationaryNon stationary
tmax is non stationaryNon stationary
tmed is non stationaryNon stationary
presMin is non stationaryNon stationary
presMax is non stationaryNon stationary
tmin is seasonalSeasonal
tmax is seasonalSeasonal
tmed is seasonalSeasonal
presMin is seasonalSeasonal
presMax is seasonalSeasonal
sol has 368 (3.0%) zerosZeros

Reproduction

Analysis started2023-03-14 15:55:20.309580
Analysis finished2023-03-14 15:55:35.405811
Duration15.1 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

tmin
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct310
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.318601
Minimum-4
Maximum29.3
Zeros11
Zeros (%)0.1%
Memory size94.8 KiB
2023-03-14T16:55:35.536793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3.7
Q18.6
median13.4
Q318.4
95-th percentile22.4
Maximum29.3
Range33.3
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation5.9101196
Coefficient of variation (CV)0.44374929
Kurtosis-0.89467669
Mean13.318601
Median Absolute Deviation (MAD)4.9
Skewness-0.095200404
Sum161421.44
Variance34.929514
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.217380672 × 10-12
2023-03-14T16:55:35.689879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 119
 
1.0%
12 111
 
0.9%
19 107
 
0.9%
8 104
 
0.9%
10 103
 
0.8%
18.4 102
 
0.8%
10.4 101
 
0.8%
18 101
 
0.8%
10.2 100
 
0.8%
14 99
 
0.8%
Other values (300) 11073
91.4%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-3.5 1
 
< 0.1%
-2.6 1
 
< 0.1%
-2.5 1
 
< 0.1%
-2 3
< 0.1%
-1.8 1
 
< 0.1%
-1.7 1
 
< 0.1%
-1.5 4
< 0.1%
-1.4 3
< 0.1%
-1.3 2
< 0.1%
ValueCountFrequency (%)
29.3 1
 
< 0.1%
28.3 1
 
< 0.1%
28.1 1
 
< 0.1%
27.8 2
< 0.1%
27.1 1
 
< 0.1%
27 2
< 0.1%
26.8 3
< 0.1%
26.6 2
< 0.1%
26.5 2
< 0.1%
26.4 4
< 0.1%
2023-03-14T16:55:35.940529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

tmax
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct385
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.746682
Minimum4
Maximum46.6
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:36.184599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14.7
Q118.9
median25
Q332.4
95-th percentile38.7
Maximum46.6
Range42.6
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation7.8609244
Coefficient of variation (CV)0.30531797
Kurtosis-1.0440479
Mean25.746682
Median Absolute Deviation (MAD)6.6
Skewness0.22440153
Sum312049.78
Variance61.794133
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.068437597 × 10-13
2023-03-14T16:55:36.322052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 116
 
1.0%
17 113
 
0.9%
19 111
 
0.9%
16 103
 
0.8%
21 96
 
0.8%
18.5 94
 
0.8%
20.5 89
 
0.7%
18.2 87
 
0.7%
20 85
 
0.7%
22 84
 
0.7%
Other values (375) 11142
91.9%
ValueCountFrequency (%)
4 1
< 0.1%
6.7 1
< 0.1%
7.2 1
< 0.1%
7.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
8 1
< 0.1%
8.3 2
< 0.1%
8.6 2
< 0.1%
8.7 1
< 0.1%
ValueCountFrequency (%)
46.6 1
 
< 0.1%
45.9 1
 
< 0.1%
45.6 1
 
< 0.1%
45.2 1
 
< 0.1%
44.9 1
 
< 0.1%
44.8 3
< 0.1%
44.5 2
< 0.1%
44.4 2
< 0.1%
44.3 2
< 0.1%
44.2 2
< 0.1%
2023-03-14T16:55:36.558186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

tmed
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct330
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.533146
Minimum2.7
Maximum36.8
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:36.806376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile9.695
Q113.875
median19
Q325.3
95-th percentile30.2
Maximum36.8
Range34.1
Interquartile range (IQR)11.425

Descriptive statistics

Standard deviation6.6794982
Coefficient of variation (CV)0.34195711
Kurtosis-1.062837
Mean19.533146
Median Absolute Deviation (MAD)5.6
Skewness0.1306624
Sum236741.73
Variance44.615697
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.046631817 × 10-14
2023-03-14T16:55:36.953774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 118
 
1.0%
12.8 107
 
0.9%
13.8 107
 
0.9%
15.2 106
 
0.9%
14.8 102
 
0.8%
14 102
 
0.8%
16 101
 
0.8%
13.4 100
 
0.8%
14.4 97
 
0.8%
15 97
 
0.8%
Other values (320) 11083
91.4%
ValueCountFrequency (%)
2.7 1
 
< 0.1%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
4 1
 
< 0.1%
4.2 1
 
< 0.1%
4.8 2
 
< 0.1%
5 3
< 0.1%
5.2 2
 
< 0.1%
5.4 7
0.1%
5.5 3
< 0.1%
ValueCountFrequency (%)
36.8 1
< 0.1%
36.3 1
< 0.1%
35.7 1
< 0.1%
35.5 1
< 0.1%
35.2 1
< 0.1%
35.1 2
< 0.1%
35 2
< 0.1%
34.9 2
< 0.1%
34.8 2
< 0.1%
34.6 2
< 0.1%
2023-03-14T16:55:37.216048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

presMin
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct477
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.6805
Minimum976.3
Maximum1035.4
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:37.451241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum976.3
5-th percentile1002.995
Q11008.3
median1010.9
Q31014.9
95-th percentile1022.5
Maximum1035.4
Range59.1
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.9723903
Coefficient of variation (CV)0.0059034351
Kurtosis1.270877
Mean1011.6805
Median Absolute Deviation (MAD)3.1
Skewness0.036380283
Sum12261568
Variance35.669446
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.86180566 × 10-25
2023-03-14T16:55:37.591221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.8 161
 
1.3%
1009 151
 
1.2%
1011.7 151
 
1.2%
1011.3 145
 
1.2%
1010.9 145
 
1.2%
1010.1 142
 
1.2%
1009.7 140
 
1.2%
1008.9 140
 
1.2%
1009.5 138
 
1.1%
1010.2 138
 
1.1%
Other values (467) 10669
88.0%
ValueCountFrequency (%)
976.3 1
< 0.1%
977.6 1
< 0.1%
982.7 1
< 0.1%
983.2 1
< 0.1%
983.3 1
< 0.1%
983.8 1
< 0.1%
984.8 2
< 0.1%
985 2
< 0.1%
985.8 2
< 0.1%
985.9 1
< 0.1%
ValueCountFrequency (%)
1035.4 1
< 0.1%
1032.4 1
< 0.1%
1032.2 1
< 0.1%
1032 1
< 0.1%
1031.4 1
< 0.1%
1031.2 1
< 0.1%
1030.8 1
< 0.1%
1030.5 1
< 0.1%
1030.1 2
< 0.1%
1030 1
< 0.1%
2023-03-14T16:55:37.850047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

presMax
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct444
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1015.8952
Minimum986.8
Maximum1038
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:38.087394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum986.8
5-th percentile1008.3
Q11012.3
median1015
Q31019.1925
95-th percentile1026.2
Maximum1038
Range51.2
Interquartile range (IQR)6.8925

Descriptive statistics

Standard deviation5.5108328
Coefficient of variation (CV)0.0054246078
Kurtosis0.56060447
Mean1015.8952
Median Absolute Deviation (MAD)3.2
Skewness0.37335966
Sum12312649
Variance30.369278
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.92911635 × 10-23
2023-03-14T16:55:38.394249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1013.8 164
 
1.4%
1012.5 144
 
1.2%
1013.4 143
 
1.2%
1012.9 142
 
1.2%
1014.5 139
 
1.1%
1012.1 138
 
1.1%
1014.9 137
 
1.1%
1013.7 136
 
1.1%
1014.6 133
 
1.1%
1015 128
 
1.1%
Other values (434) 10716
88.4%
ValueCountFrequency (%)
986.8 1
< 0.1%
990.1 1
< 0.1%
991.8 2
< 0.1%
992.2 1
< 0.1%
993 1
< 0.1%
993.7 1
< 0.1%
994.4 1
< 0.1%
995.3 1
< 0.1%
995.7 1
< 0.1%
996 1
< 0.1%
ValueCountFrequency (%)
1038 1
< 0.1%
1035.8 1
< 0.1%
1035.6 1
< 0.1%
1035.4 1
< 0.1%
1035.1 2
< 0.1%
1035 1
< 0.1%
1034.8 1
< 0.1%
1034.5 1
< 0.1%
1034.4 1
< 0.1%
1034.3 1
< 0.1%
2023-03-14T16:55:38.654666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

dir
Real number (ℝ)

Distinct216
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.83846
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:38.933398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median22
Q327
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation28.18401
Coefficient of variation (CV)0.97730636
Kurtosis1.9986298
Mean28.83846
Median Absolute Deviation (MAD)8
Skewness1.8020047
Sum349522.13
Variance794.33842
MonotonicityNot monotonic
2023-03-14T16:55:39.076119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1536
 
12.7%
22 1346
 
11.1%
23 890
 
7.3%
24 822
 
6.8%
7 699
 
5.8%
21 607
 
5.0%
6 589
 
4.9%
25 540
 
4.5%
5 420
 
3.5%
27 405
 
3.3%
Other values (206) 4266
35.2%
ValueCountFrequency (%)
1 91
 
0.8%
2 147
 
1.2%
3 226
1.9%
4 273
2.3%
4.4 1
 
< 0.1%
4.83 1
 
< 0.1%
5 420
3.5%
5.14 1
 
< 0.1%
5.38 1
 
< 0.1%
5.56 1
 
< 0.1%
ValueCountFrequency (%)
99 1536
12.7%
73.33 1
 
< 0.1%
66.71 1
 
< 0.1%
64.22 1
 
< 0.1%
64.15 1
 
< 0.1%
63.71 1
 
< 0.1%
60.5 4
 
< 0.1%
60.25 1
 
< 0.1%
58.62 1
 
< 0.1%
57.83 1
 
< 0.1%

velmedia
Real number (ℝ)

Distinct85
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0058581
Minimum0
Maximum12.8
Zeros55
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:39.214267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11.9
median2.8
Q33.9
95-th percentile5.8
Maximum12.8
Range12.8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5750876
Coefficient of variation (CV)0.52400596
Kurtosis0.81964341
Mean3.0058581
Median Absolute Deviation (MAD)1.1
Skewness0.73189366
Sum36431
Variance2.4809008
MonotonicityNot monotonic
2023-03-14T16:55:39.366186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 880
 
7.3%
3.1 870
 
7.2%
3.3 851
 
7.0%
1.9 812
 
6.7%
2.5 808
 
6.7%
1.7 787
 
6.5%
2.2 777
 
6.4%
3.9 672
 
5.5%
3.6 646
 
5.3%
1.4 646
 
5.3%
Other values (75) 4371
36.1%
ValueCountFrequency (%)
0 55
 
0.5%
0.3 152
 
1.3%
0.6 251
 
2.1%
0.8 457
3.8%
1.1 615
5.1%
1.4 646
5.3%
1.7 787
6.5%
1.9 812
6.7%
2.05 1
 
< 0.1%
2.06 1
 
< 0.1%
ValueCountFrequency (%)
12.8 1
 
< 0.1%
11.4 2
 
< 0.1%
10.8 1
 
< 0.1%
10.6 1
 
< 0.1%
10.3 3
 
< 0.1%
10 1
 
< 0.1%
9.7 4
< 0.1%
9.4 6
< 0.1%
9.2 9
0.1%
8.9 6
< 0.1%

racha
Real number (ℝ)

Distinct242
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7527145
Minimum1.9
Maximum31.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:39.516069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.3
Q17.8
median9.4
Q311.4
95-th percentile15.6
Maximum31.9
Range30
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.1800809
Coefficient of variation (CV)0.32607136
Kurtosis1.9739109
Mean9.7527145
Median Absolute Deviation (MAD)2
Skewness0.89971289
Sum118202.9
Variance10.112914
MonotonicityNot monotonic
2023-03-14T16:55:39.651091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.2 820
 
6.8%
10.3 818
 
6.7%
9.7 816
 
6.7%
8.3 772
 
6.4%
7.2 659
 
5.4%
11.4 638
 
5.3%
10.8 610
 
5.0%
8.9 550
 
4.5%
6.1 524
 
4.3%
7.8 518
 
4.3%
Other values (232) 5395
44.5%
ValueCountFrequency (%)
1.9 1
 
< 0.1%
2.5 2
 
< 0.1%
3.1 26
 
0.2%
3.6 66
 
0.5%
3.9 11
 
0.1%
4.2 111
0.9%
4.4 9
 
0.1%
4.7 183
1.5%
5 147
1.2%
5.3 210
1.7%
ValueCountFrequency (%)
31.9 1
 
< 0.1%
28.9 2
 
< 0.1%
27.8 1
 
< 0.1%
27.2 1
 
< 0.1%
26.7 1
 
< 0.1%
26.4 2
 
< 0.1%
25.8 2
 
< 0.1%
25.3 2
 
< 0.1%
24.7 6
< 0.1%
24.2 1
 
< 0.1%

sol
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.45917
Minimum0
Maximum14.4
Zeros368
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-14T16:55:39.802488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q16.3
median9.3
Q311.4
95-th percentile13.1
Maximum14.4
Range14.4
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.7486708
Coefficient of variation (CV)0.44314878
Kurtosis-0.32157922
Mean8.45917
Median Absolute Deviation (MAD)2.4
Skewness-0.75684138
Sum102525.14
Variance14.052533
MonotonicityNot monotonic
2023-03-14T16:55:39.950744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 368
 
3.0%
9.8 218
 
1.8%
9.5 216
 
1.8%
9.3 210
 
1.7%
9.2 210
 
1.7%
9.4 208
 
1.7%
12.6 203
 
1.7%
9 198
 
1.6%
10 191
 
1.6%
9.7 185
 
1.5%
Other values (241) 9913
81.8%
ValueCountFrequency (%)
0 368
3.0%
0.1 54
 
0.4%
0.2 62
 
0.5%
0.3 55
 
0.5%
0.4 42
 
0.3%
0.5 37
 
0.3%
0.6 44
 
0.4%
0.7 55
 
0.5%
0.8 46
 
0.4%
0.9 33
 
0.3%
ValueCountFrequency (%)
14.4 2
 
< 0.1%
14.3 25
0.2%
14.2 31
0.3%
14.1 41
0.3%
14 38
0.3%
13.9 35
0.3%
13.8 52
0.4%
13.7 50
0.4%
13.6 50
0.4%
13.5 51
0.4%

Interactions

2023-03-14T16:55:33.980126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:24.666544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.890261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.060711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.401732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.507332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.594574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.664289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.917872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.106709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:24.824257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.015937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.195756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.530965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.635155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.724615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.791829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.044225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.216068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:24.954357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.126039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.323589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.646181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.744588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.831011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.902340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.148873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.341763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.093705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.247814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.460732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.774059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.867384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.949848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.025468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.275426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.465402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.225142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.402727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.592791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.893039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.986732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.069927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.147588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.393082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.589156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.356795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.525377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.729276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.015639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.102681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.187773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.273018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.512764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.708288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.484656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.644718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.860461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.135215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.221219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.302150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.395199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.627484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.832013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.625164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.771905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:27.996704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.260402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.349225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.423062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.522385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.748405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:34.957242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:25.751715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:26.891702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:28.136081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:29.380093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:30.466377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:31.539688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:32.645619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-14T16:55:33.858599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-14T16:55:40.078404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
tmintmaxtmedpresMinpresMaxdirvelmediarachasol
tmin1.0000.8840.963-0.426-0.5090.1770.1980.2230.426
tmax0.8841.0000.976-0.272-0.3580.1430.0730.1100.686
tmed0.9630.9761.000-0.354-0.4400.1640.1340.1680.585
presMin-0.426-0.272-0.3541.0000.933-0.200-0.315-0.419-0.027
presMax-0.509-0.358-0.4400.9331.000-0.197-0.287-0.376-0.080
dir0.1770.1430.164-0.200-0.1971.0000.0470.0430.065
velmedia0.1980.0730.134-0.315-0.2870.0471.0000.7830.021
racha0.2230.1100.168-0.419-0.3760.0430.7831.0000.026
sol0.4260.6860.585-0.027-0.0800.0650.0210.0261.000

Missing values

2023-03-14T16:55:35.130079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-14T16:55:35.317466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tmintmaxtmedpresMinpresMaxdirvelmediarachasol
010.016.213.11016.21019.320.02.55.31.2
17.614.611.11010.61019.320.01.715.60.8
27.613.010.31008.11014.418.02.29.70.3
34.414.89.61014.41023.331.02.27.24.9
46.415.811.11023.11026.46.02.26.16.7
56.816.611.71022.61025.17.03.38.35.3
67.617.612.61024.61027.431.00.35.65.6
77.216.211.71024.91027.77.06.114.49.3
87.416.612.01025.91029.16.05.010.88.3
94.915.510.21022.21026.36.02.29.29.1
tmintmaxtmedpresMinpresMaxdirvelmediarachasol
121103.315.79.51008.01015.425.03.611.410.1
121111.315.38.31015.01019.35.01.49.210.9
121120.215.98.01011.31015.825.03.38.910.8
121130.516.98.71014.41017.730.01.15.811.0
121140.118.59.31013.91018.15.01.45.810.8
121156.817.912.41014.51017.699.01.95.05.2
121166.015.510.81011.01015.399.00.65.00.0
1211710.315.913.11008.71013.421.01.98.30.0
1211812.919.716.31009.81013.422.03.910.33.2
1211914.420.517.41011.61015.122.06.110.33.2